Since the data that we have is structured and prelabelled, we can move on to extract the features from the data. We will primarily extract certain lexical features, host-based features, and popularity-based features.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Basics of Machine Learning in Cybersecurity
- What is machine learning?
- Problems that machine learning solves
- Why use machine learning in cybersecurity?
- Current cybersecurity solutions
- Data in machine learning
- Different types of machine learning algorithm
- Algorithms in machine learning
- The machine learning architecture
- Hands-on machine learning
- Summary
- What is machine learning?
- Time Series Analysis and Ensemble Modeling
- Segregating Legitimate and Lousy URLs
- Knocking Down CAPTCHAs
- Using Data Science to Catch Email Fraud and Spam
- Efficient Network Anomaly Detection Using k-means
- Decision Tree and Context-Based Malicious Event Detection
- Catching Impersonators and Hackers Red Handed
- Changing the Game with TensorFlow
- Financial Fraud and How Deep Learning Can Mitigate It
- Case Studies
- Other Books You May Enjoy
Feature extraction
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